Quality control and filtering results from cellranger

Sample info and environment setup

PRJNA732205

setwd("/media/jacopo/Elements/re_align/MM/PRJNA732205/SAMN19314103/SRR14629341/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree 
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)

Load and process cellranger data

Load and do the QC for the cellranger data

#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial, 
    "\nNumber of genes:",  dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 10357 
## Number of genes: 36601

Quality Control

Empty cells were already filtered, check for % mt RNA and death markers:

# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 5
max_counts = 15000



# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt"))  + geom_hline(yintercept=mt_rna, linetype = "dotted")

plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1 

plot2

##  cells retained by mt RNA content ( 5 %): 5925 
##  percentage of retained cells: 57.21 %
## cells retained by counts ( 15000 ): 5882 
##  percentage of retained cells: 56.79 %

Check the distribution of the cells with low counts and control death markers:

min_counts = 800


hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")

hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))

hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)

The evident peak of cells with < 200 counts could contain dying cells.

# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)

# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)

# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)

# Print the most highly expressed genes
head(meanCounts, 30)
##     IGLC2    MALAT1    JCHAIN     IGHG3     IGHGP     RPLP1       HBB     IGLC1 
## 80.543961 30.661192 27.121417 10.028663  5.743639  3.828986  3.295974  2.884058 
##     RPL41     RPS18       B2M     IGLC3     RPL10      IGKC    RPL13A     RPS14 
##  2.781965  2.776167  2.739130  2.705314  2.655717  2.492110  2.469243  2.200000 
##     RPLP2      SSR4     RPL34     RPS19     IGHG1     RPL13    RPS27A      RPS6 
##  2.126892  2.024799  1.800644  1.700483  1.695330  1.570370  1.562319  1.341385 
##     RPS23     RPS27     RPS15    MT-ND2    RPL18A    RPS15A 
##  1.333011  1.308213  1.264734  1.254106  1.245089  1.227375
## cells retained by counts ( 800 ): 2775 
##  percentage of retained cells: 26.79 %

dir.create("result")
saveRDS(dat, file = "./result/SAMN19314103_clean_QC.Rds")

Feature selection

#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)

Data scaling

Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering

# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data

all.genes <- rownames(dat)

dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))

Dimensionality reduction

dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1 
## Positive:  PTMA, TXNIP, HNRNPA2B1, CORO1A, ARHGDIB 
## Negative:  IGLC1, IGHG3, IGHGP, HIST1H2BG, IGHG2 
## PC_ 2 
## Positive:  STMN1, HBB, HBA1, BLVRB, HBA2 
## Negative:  JUN, UBC, KLF6, H3F3B, TSC22D3 
## PC_ 3 
## Positive:  TXNIP, LINC01781, COBLL1, IGHG1, B2M 
## Negative:  LMNA, AHNAK, ANKRD28, ID2, VIM 
## PC_ 4 
## Positive:  RPS18, GAS5, RPS14, RPL18, RPS2 
## Negative:  IGHG3, IGHGP, IGHG1, IGHG2, IGLC1 
## PC_ 5 
## Positive:  HBB, CCL3, HBA1, HBA2, CCL3L1 
## Negative:  RPS14, RPS18, RPS4X, B2M, RPL5

UMAP

UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:

dat <- FindNeighbors(dat, dims = 1:20)

The graph now can be used as input for the function runUMAP()

dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)

Final plots:

## QC metrics

## markers
## Warning in FeaturePlot(dat, features = c("SDC1", "NCAM1", "HBB", "SLAMF7"), :
## All cells have the same value (0) of NCAM1.